[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNjExMDk","title","Neuro symbolic AI","state","published","contactAuthorMethod","email","aboutText"," Neuro-Symbolic AI: Fusing Neural Networks and Reasoning for Accelerating Artificial General Intelligence

What if your models could see like deep learning and think like logic?
What if they learned from data yet explained every decision?
What if the path to AGI wasn’t either/or, but both?

This book is your field guide to the most exciting frontier in artificial intelligence, the fusion of neural networks with symbolic reasoning. Neuro-symbolic AI combines the pattern-spotting power of deep learning with the structure, knowledge representation, and automated reasoning of symbolic methods to create intelligent systems that are robust, explainable, and generalize beyond their training data.

Inside, you’ll learn:

Part I – Foundations (Ch. 1–3):
Ground yourself in the basics of machine learning, neural networks (CNNs, RNNs, Transformers, transfer/meta-learning), and the pillars of symbolic AI: logic, ontologies, search, constraint satisfaction, planning, and decision theory.

Part II – Integration Techniques (Ch. 4–5):
Embed rules into networks, inject prior knowledge, and learn symbolic representations with neural embeddings, graph representations, and multi-modal concepts. Master hybrid training, evaluation, scalability, and security so your smart systems are both powerful and safe.

Part III – Reasoning Models (Ch. 6):
Step into differentiable reasoning, neural theorem provers, probabilistic logic, temporal and causal reasoning, and graph-based inference. The modern approaches that let models reason over rules with gradients and learn to prove.

Part IV – Applications (Ch. 7):
Develop Intuition on how real solutions are developed that can reduce data needs and trace their inference steps.

You’ll learn to:

Design hybrid systems that link perception to logic, search, and programs.

Inject domain knowledge and guardrails for reliability and interpretability.

Build and evaluate differentiable logic pipelines and neural theorem provers that scale.

Test for true generalization, not memorization, using principled metrics and benchmarks.

Plug in external knowledge bases and knowledge graphs for decision making under uncertainty.

Who is this for?
Researchers and engineers in AI, data science, and computer science; practitioners building ai algorithms, ai applications, ai models, and ai programming; and serious enthusiasts tracking emerging technologies, ai trends, and the future of AI. If you care about explainability, cognitive computing, computational intelligence, and real-world impact, this book speaks your language.

Why now?
From labs to industry, neuro-symbolic AI is rapidly maturing as a credible path to more general, safer, and more transparent digital intelligence. It blends learning with reasoning, a practical route toward AGI that is revolution, not evolution.

If you’re ready to build intelligent machines that both learn and reason to move beyond black boxes and toward systems that see, think, and explain, this is your playbook.

Scroll up, click “Buy Now,” and start building the next generation of AI today.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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